New Insights on the Variability of Ecosystem

New Insights on the Variability of Ecosystem Functioning Across Time Scales

Christoforos Pappas1,2, Miguel Mahecha3, David Frank4, Demetris Koutsoyiannis5 1 Département

de géographie, Université de Montréal, Montréal, QC, Canada ([email protected]) 2 Institute of environmental engineering, ETH Zurich, Zurich, Switzerland 3 Max Planck Institute for Biogeochemistry, Jena, Germany 4 Swiss Federal Research Institute, WSL, Birmensdorf, Switzerland 5 Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Greece

Research objective

2

> New Insights on the Variability of Ecosystem Functioning1 Across Time Scales

1 as seeing from various ecosystem variables, here focusing on carbon dynamics


B31E-06: Pappas et al.

Research objective

2

> New Insights on the Variability2 of Ecosystem Functioning1 Across Time Scales


2 standard deviation,  

1

N

x   N  i 1

i


2

, using probability theory and statistics


Research objective

2

> New Insights on the Variability2 of Ecosystem Functioning1 Across Time Scales3



1

N

x   N  i 1

i

2


3 five orders of magnitude, from one hour to >10 yr



Research objective

2

> New Insights4 on the Variability2 of Ecosystem Functioning1 Across Time Scales3



1

N

x   N  i 1

i

2


3 five orders of magnitude, from one hour to >10 yr 4 quantify, interpret, and model New Insights on the Variability of Ecosystem Functioning Across Time Scales


Variability across time scales - climacogram: log10(t) vs. log10(σ)


3


Data

4

EC: CO2 fluxes (NEE) Micrometeorology

European Fluxes Database:

http://www.europe-fluxdata.eu/

Carbo-Extreme:

http://www.carbo-extreme.eu/


Resolution: hourly Length: ~10+ years


Data

4

EC: CO2 fluxes (NEE) Micrometeorology

European Fluxes Database:

http://www.europe-fluxdata.eu/

Carbo-Extreme:

http://www.carbo-extreme.eu/




Data

4

EC: CO2 fluxes (NEE) Micrometeorology ERA Interim: met. variables

Resolution: hourly Length: ~10+ years Resolution: daily Length: ~40 years

Dee, D. P. et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011). New Insights on the Variability of Ecosystem Functioning Across Time Scales


Data

4


Resolution: hourly Length: ~10+ years Resolution: daily Length: ~40 years

Dee, D. P. et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011). New Insights on the Variability of Ecosystem Functioning Across Time Scales


Data

4



Resolution: daily Length: ~40 years

CRU: met. variables

Resolution: monthly Length: ~100+ years

Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014). Mitchell, T.D., Carter, T.R., Jones, P.D., and Hulme,M., 2004: A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). Tyndall Centre Working Papers. New Insights on the Variability of Ecosystem Functioning Across Time Scales


Data

4




CRU: met. variables GIMMS, MODIS: FAPAR, LAI

Resolution: monthly Length: ~100+ years Resolution: monthly Length: ~30 years

Zhu, Z. et al. Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2. Remote Sens. 5, 927–948 (2013). Pinty, B. et al. Exploiting the MODIS albedos with the Two-Stream Inversion Package (JRC-TIP): 1. Effective leaf area index, vegetation, and soil properties. J. Geophys. Res. Atmos. 116, 1–20 (2011). New Insights on the Variability of Ecosystem Functioning Across Time Scales


Data

4





Resolution: monthly Length: ~100+ years Resolution: monthly Length: ~30 years

Zhu, Z. et al. Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2. Remote Sens. 5, 927–948 (2013). Pinty, B. et al. Exploiting the MODIS albedos with the Two-Stream Inversion Package (JRC-TIP): 1. Effective leaf area index, vegetation, and soil properties. J. Geophys. Res. Atmos. 116, 1–20 (2011). New Insights on the Variability of Ecosystem Functioning Across Time Scales


Data

4





TRWs AGB

Resolution: monthly Length: ~100+ years Resolution: monthly Length: ~30 years Resolution: yearly Length: ~100+ years

Babst, F., Bouriaud, O., Alexander, R., Trouet, V. & Frank, D. Toward consistent measurements of carbon accumulation: A multisite assessment of biomass and basal area increment across Europe. Dendrochronologia 32, 153–161 (2014). Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. New Phytol. 201, 1289–1303 (2014). New Insights on the Variability of Ecosystem Functioning Across Time Scales


Data

4





TRWs AGB

Resolution: monthly Length: ~100+ years Resolution: monthly Length: ~30 years Resolution: yearly Length: ~100+ years

Babst, F., Bouriaud, O., Alexander, R., Trouet, V. & Frank, D. Toward consistent measurements of carbon accumulation: A multisite assessment of biomass and basal area increment across Europe. Dendrochronologia 32, 153–161 (2014). Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. New Phytol. 201, 1289–1303 (2014). New Insights on the Variability of Ecosystem Functioning Across Time Scales


Temporal variability: convergence in drivers…

1h

1d

1 mon

1 yr

1h

1d

1 mon

1 yr

1h

1d

1 mon

1 yr

1h

1d

1 mon

1 yr

5



1 yr

1h

1d

1 mon

1 yr

1 mon

1 yr

1h

1d

1 mon

1 yr

1 mon

1 yr

1d 1d 1d

1h 1h

6

1h

1 mon

Temporal variability: convergence in drivers… divergence in response



Patterns of variability in NEE


7


Patterns of variability in NEE

7

1d


1 yr


Patterns of variability in NEE: beyond the annual time scale


8


Patterns of variability in NEE: beyond the annual time scale


8


Combining different datasets: (a) ecosystem functioning


9


Combining different datasets: (a) ecosystem functioning GIMMS, MODIS: FAPAR, LAI

9 Resolution: monthly Length: ~30 years

TRWs, AGB


Resolution: yearly Length: ~100+ years



50 yr

10

10 yr

1 yr

1 mon




50 yr

10

10 yr

1 yr

1 mon




50 yr

10

10 yr

1 yr

1 mon



TRWs, AGB


50 yr

10

10 yr

1 yr

1 mon


Resolution: yearly Length: ~100+ years



50 yr

10

10 yr

1 yr

1 mon




50 yr

10

10 yr

1 yr

1 mon





11



sub-daily

daily – seasonal


11

seasonal – inter-annual


Combining different datasets: (b) environmental drivers | P, T, R, VPD


12




12




12




12


Resources envelope of variability

13

All sites





13



14

The variability of ecosystem functioning is confined within the range of variability of the available resources (water and energy) from hourly to >decadal time scales. New Insights on the Variability of Ecosystem Functioning Across Time Scales


Modeling the variability of ecosystem functioning across time scales

sub-daily

daily – seasonal

15





sub-daily

daily – seasonal

15




Modeling: (a) deterministic harmonics

16  T k  

50 yr

10 yr

1 yr

1 mon

1d

1h

1

k  T1 sin   k T  1 

Markonis, Y. & Koutsoyiannis, D. Climatic Variability Over Time Scales Spanning Nine Orders of Magnitude: Connecting Milankovitch Cycles with Hurst-Kolmogorov Dynamics. Surv. Geophys. (2012). doi:10.1007/s10712-012-9208-9 New Insights on the Variability of Ecosystem Functioning Across Time Scales



16  T k  

50 yr

10 yr

1 yr

1 mon

1d

1h

2

k  T2 sin   k T  2 




16  T kT  0.5 T k   0.5 T k  

T1 + T2

1

2

1

2

50 yr

10 yr

1 yr

1 mon

1d

1h

T k    0.5  1 sin     k T  1   T k   0.5  2 sin    k T  2  



Modeling: (b) simple stochastic processes

17

HK:     k H 1    k H 1

AR 1 :

WN: 

k

1

k 

1   

k 





 k 0.5

2

1     2

1   

k

k

2

 k 0.5

k : time scale H : Hurst coefficient ρ : lag-1 autocorrelation coefficient

Koutsoyiannis, D. HESS Opinions ‘A random walk on water’. Hydrol. Earth Syst. Sci. 14, 585–601 (2010). Dimitriadis, P. & Koutsoyiannis, D. Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst–Kolmogorov processes. Stoch. Environ. Res. Risk Assess. (2015). doi:10.1007/s00477-015-1023-7 New Insights on the Variability of Ecosystem Functioning Across Time Scales



sub-daily

daily – seasonal

18


50 yr

10 yr

1 yr

1 mon

1d

1h

T1 + T2




sub-daily

daily – seasonal

18


(k )  EcoFun  f  w1 , w2 , w3 , w4 ,  , H , k   w1 ( k )  w2 HK  w3 ( k )  w4 ( k )  (k )

AR(1)

    w1  0.5 k   

1    2

T1:1d

T2:1yr

1       2

1   

k

k

2

  w k H 1  w T1 sin   k   w T2 sin   k   3 k  T  4 k  T  2   1   2    



Conclusions

19 The variability of ecosystem functioning across time scales is confined within the range of variability of the environmental drivers.



Conclusions

19 The variability of ecosystem functioning across time scales is confined within the range of variability of the environmental drivers. An overview of the variability of ecosystem functioning across time scales spanning five orders of magnitude is presented combining multivariate datasets.



Conclusions


The variability of ecosystem functioning across time scales can be adequately represented with surprisingly simple models.



Conclusions


The variability of ecosystem functioning across time scales can be adequately represented with surprisingly simple models.

Implications: - Long-term terrestrial carbon source-sink dynamics and the related CO2 variability - Benchmarking of process-based terrestrial ecosystem models



Thank you! Acknowledgements - EC site PIs - CRU and ERA Intermin - GIMMS FPAR3g & LAI3g (Ranga B. Myneni) and MODIS TIP (JRC) - Flurin Babst for providing the AGB data Funding: - Stavros Niarchos Foundation and ETH Zurich Foundation

Christoforos Pappas, Miguel Mahecha, David Frank, Demetris Koutsoyiannis